16 citations · 32 across the 8 of their papers we have counts for
8 papers
Exploring Line Bundle Standard Models with Transformers
Jacky H. T. Yip, Alessandro Mininno, Gary Shiu
We propose a Transformer-based Reinforcement Learning architecture, "LB-Explorer", to search for heterotic line bundle standard models arising from compactifications on smooth Cala…
Generating Special Triangulations with Transformers
Charles Arnal, Jacky H. T. Yip, François Charton +1
Triangulations, i.e., well-structured decompositions of geometric objects into triangle-like pieces, are central objects in many domains of mathematics and physics. In particular,…
Primordial non-Gaussianity -- Fast simulations and persistent summary statistics
Juan Calles, Gabriella Contardo, Jorge Noreña +2
We investigate the sensitivity of topological and traditional summary statistics to primordial non-Gaussianity (PNG) using two suites of simulations. First, we introduce a new simu…
Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformers
Jacky H. T. Yip, Charles Arnal, François Charton +1
Fine, regular, and star triangulations (FRSTs) of four-dimensional reflexive polytopes give rise to toric varieties, within which generic anticanonical hypersurfaces yield smooth C…
Cosmology with Persistent Homology: Parameter Inference via Machine Learning
Juan Calles, Jacky H. T. Yip, Gabriella Contardo +3
Building upon [2308.02636], we investigate the constraining power of persistent homology on cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pi…
Cosmology with Persistent Homology: a Fisher Forecast
Jacky H. T. Yip, Matteo Biagetti, Alex Cole +2
Persistent homology naturally addresses the multi-scale topological characteristics of the large-scale structure as a distribution of clusters, loops, and voids. We apply this tool…
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure
Jacky H. T. Yip, Adam Rouhiainen, Gary Shiu
The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topolo…
From Dark Matter to Galaxies with Convolutional Neural Networks
Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang +7
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations. However, to co…